CN113723030A - Actual gas physical property simulation method and system based on computational fluid dynamics - Google Patents

Actual gas physical property simulation method and system based on computational fluid dynamics Download PDF

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CN113723030A
CN113723030A CN202111209689.5A CN202111209689A CN113723030A CN 113723030 A CN113723030 A CN 113723030A CN 202111209689 A CN202111209689 A CN 202111209689A CN 113723030 A CN113723030 A CN 113723030A
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齐建荟
徐进良
韩奎华
秦侃
肖永清
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Shandong University
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Abstract

The invention discloses a method and a system for simulating actual gas physical properties based on computational fluid dynamics, which comprises the following steps: acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine; carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model; and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine. The method is coupled with a Riemann problem solver, an actual gas physical property lookup table method, a flux format suitable for actual gas physical properties, a difference error reduction and other technologies, can simulate and simulate the actual gas physical property Riemann problem quickly and reliably, and particularly aims at solving the high-power-density compact turbomachine problem.

Description

Actual gas physical property simulation method and system based on computational fluid dynamics
Technical Field
The invention relates to the field of computational fluid dynamics simulation based on actual gas physical properties, in particular to a method and a system for actual gas physical property simulation based on computational fluid dynamics.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Supercritical carbon dioxide (S-CO)2) Power cycles have attracted considerable attention in recent years due to their compact structure, higher efficiency and simpler cycle layout. S-CO2Is considered an excellent working fluid because of its many advantages, such as the combination with H2O or other fluids tend to reach critical points, availability, and low global warming potential compared to other gases. For power cycles in the range of 0.1 to 25MW, S-CO is used2Allowing the paradigm to shift to using an efficient radial turbine. Because of the combined effects of the high density working fluid, the relatively low flow rate, and the low specific speed, a more power dense turbomachine may be used. Similar attention has been given to Organic Rankine Cycles (ORC), which utilize thermodynamic properties to ensure a better match between a low temperature heat source and a high density working fluid.
In these cycles, the thermodynamic phenomena of the non-ideal gas are of necessity. Especially for S-CO near critical point of operation condition2Compressor, S-CO2The flow characteristics are significantly affected by non-ideal gas dynamics of (A). Similarly, in high pressure ratio ORC turbines, shock waves are typically generated as the fluid passes through the turbine passages. They are the result of sudden expansion of the working fluid flowing through the nozzle or vane tip. Unless these components can be properly simulated and designed, all gains in cycle efficiency are lost due to poor component performance.
Therefore, there is a need to accurately predict fluid flow under these non-ideal conditions, to correctly capture the actual physical properties of dense/supercritical fluids, and to address compressible high mach number flows with different thermodynamic behaviors from the ideal gas, thus leading to the field of non-ideal compressible fluid dynamics (NICFD) research.
In the design phase, accurate NICFD simulations are performed for S-CO operating in the supercritical region or near critical point2Circulation and ORC components are critical. Some authors report numerical solutions to these highly compressible flows using Computational Fluid Dynamics (CFD), but most of these studies used numerical methods developed for ideal gases. For ORC and S-CO2Reliable NICFD simulation of such flows remains a challenge in the field of research because of the need for complex tools and highly complex experimental calibration thermodynamic models. Therefore, there is a need for a simulation tool that can accurately predict non-ideal fluid flow during the design phase.
In practical applications, it is common to perform CFD simulations on such flows using ideal gas correlations with modified gas constants and isentropic coefficients. However, these assumptions may introduce errors due to the limited accuracy of the gas property approximation. This is particularly important when studying compressible flows with non-ideal gas zone characteristics, where non-ideal gas phenomena change the flow relationships. Poor assessment of total pressure and temperature values leads to poor predictions of losses, specific work, heat exchange and density, which affect the calculation of momentum components and thus the predicted flow structure. Therefore, the CFD solver must use the most accurate true gas properties to correctly solve the flow.
Currently, there are several methods for CFD solvers to capture non-ideal gas properties. The thermophysical properties of the existing working medium comprise the following non-ideal state equation: Peng-Robinson (PR), Redlich-KWong (RK), and polynomial transport and thermodynamic properties. However, for S-CO2Or turbomachinery simulation in ORC applications, requires a fast non-ideal gas flow solver, plus a non-ideal gas riemann flux calculator that can select any gas model. Currently, none of the existing solvers provide for solving compressible Riemann problems and using non-ideal state equations simultaneouslyThe ability of the cell to perform. In addition, the non-ideal state equation to be realized needs to be solved iteratively, which results in large computational burden and slow solving process. The existing commercial CFD software has various limitations in the aspects of flux format, actual physical property parameter capture and optimization algorithm combination, and an internal operation method is difficult to obtain, like a black box, so that the existing commercial CFD software cannot be applied to the research of the front-edge exploration problem.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a method and a system for simulating the physical properties of actual gas based on computational fluid mechanics;
in a first aspect, the invention provides a method for simulating actual gas physical properties based on computational fluid dynamics;
the actual gas physical property simulation method based on computational fluid dynamics comprises the following steps:
acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine;
carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model;
and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine.
In a second aspect, the present invention provides a computational fluid dynamics based actual gas properties simulation system;
an actual gas physical property simulation system based on computational fluid dynamics, comprising:
an acquisition module configured to: acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine;
a modeling module configured to: carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model;
a simulation module configured to: and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine.
In a third aspect, the present invention further provides an electronic device, including:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention also provides a storage medium storing non-transitory computer readable instructions, wherein the non-transitory computer readable instructions, when executed by a computer, perform the instructions of the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the method is coupled with a Riemann problem solver, an actual gas physical property lookup table method, a flux format suitable for actual gas physical properties, a difference error reduction and other technologies, can simulate and simulate the actual gas physical property Riemann problem quickly and reliably, and particularly aims at solving the high-power-density compact turbomachine problem.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart illustrating an equivalent calculation implementation according to a first embodiment of the present invention;
fig. 2(a) and fig. 2(b) are schematic diagrams of the riemann problem in the physical space and on the CFD mesh according to the first embodiment of the present invention;
FIG. 3 is a graph of a numerical shading resulting from the use of the method of the present invention according to a first embodiment of the present invention;
FIG. 4 is a graph comparing simulated data and experimental data for an air nozzle in accordance with a first embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
All data are obtained according to the embodiment and are legally applied on the data on the basis of compliance with laws and regulations and user consent.
Example one
The embodiment provides an actual gas physical property simulation method based on computational fluid dynamics;
as shown in fig. 1, the method for simulating actual gas properties based on computational fluid dynamics includes:
s101: acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine;
s102: carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model;
s103: and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine.
In the embodiment, the computational fluid dynamics simulation is performed on the physical properties of the actual gas in the turbine by adopting the algorithm in the CFD solver, so that the accurate simulation can be realized, the problem of compressible Riemann rotating machinery based on the physical properties of the actual gas can be solved, and the mechanical performance of the turbine impeller can be evaluated and optimized according to the flow field distribution condition in the turbine.
Further, the S102: the divided grid is required to perform computational fluid dynamics simulation. Fluid mechanics simulation requirements, such as meshes, require a set mass to be achieved.
Further, the step S103: performing computational fluid mechanics simulation on actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain flow field distribution and flow conditions inside the turbine; the method specifically comprises the following steps:
s1031: aiming at the Riemann problem, constructing an internal energy field E, a pressure field p, a temperature field T, a local sound velocity field a, a density velocity vector field rho U, a velocity field U, a density energy field rho E and an enthalpy field h to initialize computational fluid mechanics simulation, and performing table lookup on initial actual gas physical properties by a table lookup method by taking p and E as input parameters;
s1032: updating parameters on the boundary condition in a Multi-Reference frame MRF (Multi-Reference Fram), initializing physical simulation time and giving a calculation step length delta T; the boundary conditions refer to the inlet and outlet parameters of the turbine three-dimensional model and the wall surface of the turbine; the parameters include temperature and pressure;
s1033: judging whether the current iteration number i is less than the number of the sub-cycles; if so, go to S1034; if not, finishing the calculation of a physical time step;
s1034: judging whether the current run of the Runge-Kutta accelerated iteration number beta is smaller than a set threshold value; if so, proceed to S1035; if not, adding 1 to the current iteration number i, and returning to S1033; the set threshold is equal to 4;
s1035: correcting and updating the numerical value on the simulation boundary by using the pressure field p, the velocity field U and the internal energy field e and searching the actual gas physical property table;
s1036: calculating Godunov flux and solving a control equation;
s1037: updating the coefficient of the multi-reference-frame MRF (updating the position of the relative network at the next time step according to the pressure field p, the speed field U and the rotating speed of the turbine) so as to solve the flow state of the actual gas under the rotating reference frame;
s1038: updating the angle rho and the internal energy field e by using the updated coefficient of the multi-reference-system MRF;
s1039: updating the pressure field p by using a secant method through the updated rho and internal energy field e;
s1040: updating the enthalpy field h, the temperature field T, the local sound velocity fields a and rho and the values on the boundary by using the pressure field p and the internal energy field e;
s1041: updating the MRF coefficient and the turbulence characteristic value by solving a control equation by using the physical property parameters obtained in the step S1040;
s1042: add one to the iteration number β, and return to S1034.
Further, the S1036: calculating Godunov flux and solving a control equation; the method specifically comprises the following steps:
continuity equations for multiple reference frames:
Figure BDA0003308387230000071
wherein v isrelIs the relative velocity vector, t is the time step,
Figure BDA0003308387230000075
is a differential mathematical operator;
momentum equations for multiple reference frames:
Figure BDA0003308387230000072
where v is the velocity vector, Ω is the angular velocity vector, and σ is the total shear stress vector;
energy equations for multiple reference systems:
Figure BDA0003308387230000073
where t is the time step, vrotIs the rotation velocity vector, λ is the thermal conductivity, μ is the kinematic viscosity, μTIs turbulent dynamic viscosity and TKE is turbulent kinetic energy.
Further, the S1039: updating the pressure field p by a secant method; the method specifically comprises the following steps:
Figure BDA0003308387230000074
wherein L is a corresponding look-up table, Lρ(e,pn) Means that e and p are usednFind their corresponding p values.
pn-1＝(1+δ)·pn (5)
Where δ is a variation value, e.g. 1X 10-6。
The MRF coefficients and turbulence characteristic values are corrected and the internal loop count β of the logg-kuttage-Kutta is updated, if β reaches 4, a new physical time step calculation is entered and i is increased by 1.
After the calculation is finished, it can be seen that the numerical shading map calculated by the method is substantially coincident with the experimentally obtained shading map.
The riemann problem, which is a problem of supersonic speed encountered during the CFD simulation, is mathematically expressed as a problem of discontinuity of an initial value, as shown in the following equation (6), and as shown in fig. 2(a) and 2 (b).
Figure BDA0003308387230000081
Where U is a flow parameter vector, x is a certain point on a two-dimensional coordinate axis, and i and j are respectively indicated on the left or right side of the point (x, 0).
The CFD software may be OpenFOAM, Eilmer4, or business software such as ANSYS, as long as it can be implanted according to the method provided by the present invention. The programming language to be implanted should not be limited, and may be C + +, Python, or the like.
The actual gas physical properties are obtained by a table look-up method, and the table look-up method needs two sets of physical property parameter tables, one is a table based on p and e, and the other is a table based on rho and e.
The actual gas property sources in the property table are not limited to the database such as REFPROP or CoolProp from NIST, and may be created by the user.
The results of the examples can be obtained by post-processing with CFD software, as shown in fig. 3 and 4.
Fig. 3 and 4 illustrate that the method can accurately simulate the riemann problem based on the physical properties of the actual gas.
The flux format of actual gas properties is modified based on the HLLC ALE format, which is based on the unsteady state compressible euler equation as follows:
Figure BDA0003308387230000091
where Ω is the mathematical representation of the simulated region, Γ is the mathematical representation of the boundary of the simulated region, F is the non-viscous flux vector, and is defined by F and U by the following equation:
Figure BDA0003308387230000092
n is the normal vector of the surface, E is the total internal energy,
Figure BDA0003308387230000097
refers to the current grid rotation speed vector.
The HLLC ALE flux solver is defined by:
Figure BDA0003308387230000093
wherein, the number indicates the flux superposition area, namely the superposition area of the pressure waves at the left side and the right side, namely the position of the discontinuity in the future time step required to be solved in the area;
Figure BDA0003308387230000094
s is the transmission speed of waves in the Riemann problem, q is the vertical relative speed in a numerical solving system, and the lower corner mark K can refer to i or j and refers to the left side and the right side of a calculated flux plane.
Figure BDA0003308387230000095
Thus, the propagation velocity of the wave in the Riemann problem can be calculated as:
Figure BDA0003308387230000096
in the finite volume method, as shown in fig. 2(a) and 2(b), the flux between the left and right grids can be obtained by the following equation:
Figure BDA0003308387230000101
Figure BDA0003308387230000102
the speed and sound velocity approximation term of Roe can be obtained by the following formula:
Figure BDA0003308387230000103
Figure BDA0003308387230000104
core, because in solving for ηγFunction:
Figure BDA0003308387230000105
the approximation method is used, and for rho, the method described by the equation can be used, so that the numerical dependence is removed when the numerical values on the left side and the right side of the grid interface are calculated, and the corresponding numerical values on the left side and the right side of the grid interface can be given by directly using a table look-up method.
Example two
The embodiment provides an actual gas physical property simulation system based on computational fluid dynamics;
an actual gas physical property simulation system based on computational fluid dynamics, comprising:
an acquisition module configured to: acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine;
a modeling module configured to: carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model;
a simulation module configured to: and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine.
It should be noted here that the above-mentioned obtaining module, modeling module and simulation module correspond to steps S101 to S103 in the first embodiment, and the above-mentioned modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The actual gas physical property simulation method based on computational fluid mechanics is characterized by comprising the following steps:
acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine;
carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model;
and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine.
2. The method of computational fluid dynamics-based simulation of actual gas properties of claim 1 wherein the partitioned grid is to meet computational fluid dynamics simulation requirements.
3. The method for physical simulation of actual gas based on computational fluid dynamics as claimed in claim 1, wherein the physical simulation of actual gas in the supercritical carbon dioxide turbine is performed based on the obtained inlet and outlet parameters and the three-dimensional model after meshing to obtain the flow field distribution and flow conditions inside the turbine; the method specifically comprises the following steps:
(1) aiming at the Riemann problem, constructing an internal energy field E, a pressure field p, a temperature field T, a local sound velocity field a, a density velocity vector field rho U, a velocity field U, a density energy field rho E and an enthalpy field h to initialize computational fluid mechanics simulation, and performing table lookup on initial actual gas physical properties by a table lookup method by taking p and E as input parameters;
(2) updating parameters on the inner boundary condition of the multi-reference-system MRF, initializing physical simulation time and setting a calculation step length delta T; the boundary conditions refer to the inlet and outlet parameters of the turbine three-dimensional model and the wall surface of the turbine; the parameters include temperature and pressure;
(3) judging whether the current iteration number i is less than the number of the sub-cycles; if yes, entering the next step; if not, finishing the calculation of a physical time step;
(4) judging whether the current run of the Runge-Kutta accelerated iteration number beta is smaller than a set threshold value; if yes, entering the next step; if not, adding 1 to the current iteration number i, and returning to the previous step;
(5) correcting and updating the numerical value on the simulation boundary by using the pressure field p, the velocity field U and the internal energy field e and searching the actual gas physical property table;
(6) calculating Godunov flux and solving a control equation;
(7) updating the coefficient of the MRF of the multiple reference systems to solve the flow state of the actual gas under the rotating reference system;
(8) updating the angle rho and the internal energy field e by using the updated coefficient of the multi-reference-system MRF;
(9) updating the pressure field p by using a secant method through the updated rho and internal energy field e;
(10) updating the enthalpy field h, the temperature field T, the local sound velocity fields a and rho and the values on the boundary by using the pressure field p and the internal energy field e;
(11) updating MRF coefficients and turbulence characteristic values by solving a control equation by using the physical property parameters obtained in the last step;
(12) and (4) adding one to the iteration number beta and returning.
4. The computational fluid dynamics-based simulation method of actual gas properties as claimed in claim 3 wherein said Godunov flux is calculated and the governing equation is solved; the method specifically comprises the following steps:
continuity equations for multiple reference frames:
Figure FDA0003308387220000021
wherein v isrelIs the relative velocity vector, t is the time step,
Figure FDA0003308387220000022
is a differential mathematical operator;
momentum equations for multiple reference frames:
Figure FDA0003308387220000023
where v is the velocity vector, Ω is the angular velocity vector, and σ is the total shear stress vector;
energy equations for multiple reference systems:
Figure FDA0003308387220000031
where t is the time step, vrotIs the rotation velocity vector, λ is the thermal conductivity, μ is the kinematic viscosity, μTIs turbulent dynamic viscosity and TKE is turbulent kinetic energy.
5. A method for computational fluid dynamics based simulation of actual gas properties according to claim 3 wherein the pressure field p is updated by means of a secant method using the updated p and internal energy fields e; the method specifically comprises the following steps:
Figure FDA0003308387220000032
wherein L is a corresponding look-up table, Lρ(e,pn) Means that e and p are usednSearching for their corresponding rho values;
pn-1＝(1+δ)·pn (5)
where δ is a variation value, e.g. 1X 10-6。
6. The method of claim 3, wherein the Riemann problem is a supersonic problem encountered during the CFD simulation, and is mathematically expressed as a discontinuity in initial values, as shown in equation (6):
Figure FDA0003308387220000033
where U is a flow parameter vector, x is a certain point on a two-dimensional coordinate axis, and i and j are respectively indicated on the left or right side of the point (x, 0).
7. A computational fluid dynamics-based simulation method of actual gas properties according to claim 3 wherein the flux format of the actual gas properties is modified based on HLLC ALE format based on unsteady state compressible euler equations as follows:
Figure FDA0003308387220000041
where Ω is the mathematical representation of the simulated region, Γ is the mathematical representation of the boundary of the simulated region, F is the non-viscous flux vector, and is defined by F and U by the following equation:
Figure FDA0003308387220000042
wherein n is the normal vector of the surface, E is the total internal energy,
Figure FDA0003308387220000047
refers to the current grid rotation velocity vector;
the HLLC ALE flux solver is defined by equation (9):
Figure FDA0003308387220000043
wherein, the number indicates the flux superposition area, namely the superposition area of the left and right pressure waves, namely the position of the discontinuity in the future time step required to be solved in the area;
Figure FDA0003308387220000044
s is the transmission speed of waves in the Riemann problem, q is the vertical relative speed in a numerical solving system, and lower corner marks K refer to i or j and refer to the left side and the right side of a calculated flux plane;
Figure FDA0003308387220000045
from this, the propagation velocity of the wave in the riemann problem is calculated as:
Figure FDA0003308387220000046
for the finite volume method, the flux between the left and right grids is calculated by the following formula:
Figure FDA0003308387220000051
Figure FDA0003308387220000052
wherein, the approximate term of the speed and the sound velocity of Roe is obtained by the following formula:
Figure FDA0003308387220000053
Figure FDA0003308387220000054
core, because in solving for ηγFunction:
Figure FDA0003308387220000055
8. an actual gas physical property simulation system based on computational fluid dynamics, comprising:
an acquisition module configured to: acquiring inlet and outlet parameters of a supercritical carbon dioxide turbine, wherein the inlet and outlet parameters comprise: temperature, pressure and rotational speed of the turbine;
a modeling module configured to: carrying out full three-dimensional modeling on the supercritical carbon dioxide turbine, and carrying out spatial grid division on the established three-dimensional model;
a simulation module configured to: and performing computational fluid mechanics simulation on the actual gas physical properties in the supercritical carbon dioxide turbine based on the acquired inlet and outlet parameters and the three-dimensional model after grid division to obtain the flow field distribution and the flow condition in the turbine.
9. An electronic device, comprising:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of any of claims 1-7.
10. A storage medium storing non-transitory computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by a computer, perform the instructions of the method of any one of claims 1-7.
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CN115938494B (en) * 2022-11-24 2024-01-09 中国科学院大气物理研究所 DCU acceleration calculation method, equipment and storage medium of gas phase chemical module
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